Reads a queue and acts on it
An inbox, a form, a folder, a webhook. The agent classifies what arrived, extracts what matters, files it where it belongs, and escalates what it is not sure about.
Operations teams
Internal tools and agents for the work nobody enjoys.
The repetitive, high-volume, judgement-light work that consumes your team and appears in no job description.
The situation
Every operations team has a list of tasks that are too varied for a rule engine, too frequent to ignore, and too dull to keep a good person doing. They get absorbed quietly, by whoever has capacity, and they are the reason the team is always slightly behind.
Where the time goes
- Copying data between systems that were never going to be integrated
- Reading incoming documents and email to work out where each one should go
- Chasing people for information, then chasing them again
- Compiling the same report every week from the same four places
- First-line questions that have documented answers nobody can find
What an agent actually is here
Not a chatbot bolted onto your website. A process with credentials, limits, a schedule and an audit trail — that happens to use a model to make judgement calls.
Understands your documents
Answers grounded in your own policies, contracts and manuals — with the source shown, so a person can check the answer rather than trust it.
Works across systems that do not talk
Reads from one, writes to another, reconciles the difference, and reports what did not match instead of silently guessing.
Runs on a schedule or on an event
Nightly reconciliation, hourly sync, or triggered the moment something arrives. It does not need anyone to remember to run it.
Knows when to ask a person
Confidence thresholds and explicit escalation. The design goal is not full autonomy — it is handling the eighty per cent that is unambiguous and routing the rest to someone qualified.
Leaves a trail
Every action logged with the input, the reasoning and the outcome. When someone asks why the agent did that, there is an answer.
For example
What this looks like in practice
Applicant and lead screening
Incoming applications read against the actual requirements, ranked with reasons, and surfaced to a human who makes the decision. The agent does the reading, not the deciding.
Invoice and document intake
Suppliers send documents in whatever form they like. Fields are extracted, matched against purchase orders, and only the mismatches reach a person.
Internal support
A first line that answers from your own documentation, cites the source, and hands over to a person the moment it is out of its depth.
Recurring reporting
The weekly numbers pulled from every source they live in, assembled, checked for the obvious errors, and delivered before the meeting rather than during it.
Why this
Why this is different from buying an AI tool
The gap between a demo and something you can let near your production data is entirely made of unglamorous engineering.
- Credentials are scoped to exactly what the agent needs, and nothing else
- Spending limits mean a loop cannot become an invoice
- Every action is logged and reviewable, so behaviour can be explained
- A person stays in the loop wherever the cost of being wrong is real
- It is monitored like production software, because that is what it is
Questions people ask
What if the agent gets it wrong?
It will, sometimes — which is why the design starts with what happens then. Confidence thresholds, escalation to a person, and a full log of what it did and why. Agents are given autonomy in proportion to the cost of being wrong.
Will our data be used to train a model?
No. Your data is used to answer your questions and nothing else.
How do we stop AI costs running away?
Spending limits with alerts, model choice matched to the task, and caching for repeated work. It is watched deliberately because it is the line most likely to surprise people.
Can it work with our existing tools?
That is usually the whole point — the value is in connecting systems that were never going to be integrated properly.